Design and Development of a Hardware Test Platform for Multi-Agent Control Algorithms Testing and Validation - Tanveer, Sarmad (Université d'Ottawa / University of Ottawa, 2021)
Multi-robot networks are used in a variety of military and civilian applications such as harbour protection, perimeter surveillance, search & rescue missions and cooperative estimation, among others. In order to develop functional multi-robot networks to achieve these tasks, a combination of theoretical and experimental work is required. Theoretical research aims to model the open and closed loop dynamics of multi-robot systems and to develop corresponding control algorithms for tackling the previously mentioned tasks. Experimental work focuses on the hardware or simulated implementations to test and evaluate the algorithms’ performance, and eventually inform refinements of theoretical algorithms to adapt to hardware imposed intrinsic constraints. As theoretical and algorithmic research in the field of multi-robot networks matures, a need for experimental validation of a variety of sophisticated algorithmic tools becomes apparent, with the two aspects co-developing to inform theoretical refinements that account for hardware induced constraints, and possible technological advances suggested by theoretical predictions. This thesis contributes a design for a hardware test platform for evaluating and implementing algorithms in the field of multi-robot networks. The test platform design implements an off the market robot solution for the robotic agents, discussing various additional embedded frameworks that allow for capabilities such as indoor agent localization, inter-agent wireless communication and agent locomotion, with the goal of understanding if a combination of existing market and academic technologies can be used to develop a cost effective hardware multi-agent test platform. The proposed hardware design is then validated on previously developed multiagent area coverage control and optimization algorithms. More specifically, the hardware test platform is used to implement various optimal coverage problems with time invariant risk density distributions. Both uniform and nonuniform risk density distribution scenarios are considered. These experimental results are compared with simulations to assess if the proposed hardware test platform design can plausibly reproduce behaviours that are consistent with theoretical predictions of area coverage control algorithms. Future work will include the extension of the testing capabilities of this test platform to a larger class of multi-agent control algorithms.
Keywords: Robotics; Machine Learning; Swarm; Arduino; XBee; Control Systems; Mechanical; Sensors; Algorithms; Python; Tensorflow; Controllers